Device-to-Device (D2D) communication has emerged as a promising technology for optimizing spectral efficiency in future cellular networks. D2D takes advantage of the proximity of communicating devices for efficient utilization of available resources, improving data rates, reducing latency and increasing system capacity. The research community is actively investigating the D2D paradigm to realize its full potential and enable its smooth integration into the future cellular system architecture. Existing surveys on this paradigm largely focus on interference and resource management. We review recently proposed solutions in over explored and under explored areas in D2D. These solutions include protocols, algorithms, and architectures in D2D. Furthermore, we provide new insights on open issues in these areas. Finally, we discuss potential future research directions.

Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

Trust is a salient feature in the context of health care, which is characterized by uncertainty and an element of risk. It is a fundamental requirement for the acceptance and adoption of new services related to health care. Soft trust, based on social control mechanisms, has yielded to evidence-based trust management, where the level of trust is explicitly computed by a trust engine termed the Trust and Reputation System (TRS). In e-health, soft trust can be used for access control, for assessing the quality of data represented in Electronic Health Records (EHRs), to address privacy and security requirements of healthcare systems, and to compute the credibility of an entity in the presence of unknown, possibly harmful entities. Despite the importance of soft trust in healthcare, related literature that explore soft trust issues, associated challenges and requirements are largely missing. To address this deficiency, the main contributions of this paper include: (i) taxonomy related to the soft trust in healthcare systems, (ii) reference model for measuring the performance and features of TRSs, (iii) future areas of research related to the soft trust in healthcare.